Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Effective questionnaire-based prediction models for type 2 diabetes across several ethnicities:a model development and validation study

Background: Type 2 diabetes disproportionately affects individuals of non-White ethnicity through a complex interaction of multiple factors. Therefore, early disease detection and prediction are essential and require tools that can be deployed on a large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes prevalence and incidence for multiple ethnicities.Methods: In this proof of principle analysis, logistic regression models to predict type 2 diabetes prevalence and incidence, using questionnaire-only variables reflecting health state and lifestyle, were trained on the White population of the UK Biobank (n = 472,696 total, aged 37–73 years, data collected 2006–2010) and validated in five other ethnicities (n = 29,811 total) and externally in Lifelines (n = 168,205 total, aged 0–93 years, collected between 2006 and 2013). In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Type 2 diabetes prevalence in the UK Biobank ranged between 6% in the White population to 23.3% in the South Asian population, while in Lifelines, the prevalence was 1.9%. Predictive accuracy was evaluated using the area under the receiver operating characteristic curve (AUC), and a detailed sensitivity analysis was conducted to assess potential clinical utility. We compared the questionnaire-only models to models containing physical measurements and biomarkers as well as to clinical non-laboratory type 2 diabetes risk tools and conducted a reclassification analysis.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC = 0.901) and eight-year incidence (AUC = 0.873) in the White UK Biobank population. Both models replicated well in the Lifelines external validation, with AUCs of 0.917 and 0.817 for prevalence and incidence, respectively. Both models performed consistently well across different ethnicities, with AUCs of 0.855–0.894 for prevalence and 0.819–0.883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 additional cases. Model performance improved with the addition of blood biomarkers but not with the addition of physical measurements.Interpretation: Our findings suggest that easy-to-implement, questionnaire-based models could be used to predict prevalent and incident type 2 diabetes with high accuracy across several ethnicities, providing a highly scalable solution for population-wide risk stratification. Future work should determine the effectiveness of these models in identifying undiagnosed type 2 diabetes, validated in cohorts of different populations and ethnic representation.Funding: University Medical Center Groningen

Developing Effective Questionnaire-Based Prediction Models for Type 2 Diabetes for Several Ethnicities

Background: Type 2 diabetes disproportionately affects individuals of non-white ethnicity through a complex interaction of multiple factors. Early disease prediction and detection is therefore essential and requires tools that can be deployed at large scale. We aimed to tackle this problem by developing questionnaire-based prediction models for type 2 diabetes for multiple ethnicities.Methods: Logistic regression models, using questionnaire-only features, were trained on the White population of the UK Biobank, and validated in five other ethnicities and externally in Lifelines. In total, 631,748 individuals were included for prevalence prediction and 67,083 individuals for the eight-year incidence prediction. Predictive accuracy was assessed and a detailed sensitivity analysis was conducted to assess potential clinical utility. Furthermore, we compared the questionnaire algorithms to clinical non-laboratory type 2 diabetes risk tools.Findings: Our algorithms accurately predicted type 2 diabetes prevalence (AUC=0·901) and eight-year incidence (AUC=0·873) in the White UK Biobank population. Both models replicate well in Lifelines, with AUCs of 0·917 and 0·817 for prevalence and incidence. Both models performed consistently well across ethnicities, with AUCs of 0·855 to 0·894 for prevalence and from 0·819 to 0·883 for incidence. These models generally outperformed two clinically validated non-laboratory tools and correctly reclassified >3,000 type 2 diabetes cases. Model performance improved with the addition of blood biomarkers, but not with the addition of physical measurements.Interpretation: Easy-to-implement, questionnaire-based models can predict prevalent and incident type 2 diabetes with high accuracy across all ethnicities, providing a highly-scalable solution for population-wide risk stratification

The pattern and functional consequence of Killer Immunoglobulin-like Receptor expression on T cells.

Killer immunoglobulin-like receptors (KIRs) are a family of proteins expressed on human natural killer cells and a subset of T cells. Several inhibitory KIRs have been shown to recognise MHC class I molecules (predominantly HLA-C), with their engagement preventing target cell lysis. The ligand(s) and function(s) of activating KIRs, however, are less well characterised. Genetic studies of the association of KIRs with disease have identified an association with viral infections and autoimmune disease and this implicates that these proteins are important in human health. This thesis was concerned with an investigation of the factors that determine KIR expression on lymphocytes, and how this might influence the cellular functional response. In my initial work I produced soluble recombinant forms of activating and inhibitory KIRs and studied the biophysical interaction of these proteins with HLA-C molecules. I saw some evidence that KIR2DS2 binds to the HLA-C group 1 allele HLA-Cw*0702, supporting the idea that HLA-C alleles are a true ligand for stimulatory KIRs. I then went on to make a detailed 11 colour flow cytometric analysis of the expression of KIR proteins in healthy individuals. I was able to show that total, and individual, KIR protein expression was correlated and defined a pattern of dominance on lymphoid subsets. I then went on to study the distribution of KIR expression on discrete memory T cell subsets and showed that they were found predominantly on late differentiating CD45RA+ T cells. Interestingly there was also considerable expression on central memory CD8+ T cells although the biological basis for this is unclear. I demonstrated that age and CMV infection have a marked effect on KIR expression and I speculate on the reason for this. Finally I studied KIR expression on CMV-specific T cell clones in order to undertake a functional analysis of the consequence of KIR expression. I observed that KIR expression increased when cells were cultured in vitro but I could not detect any difference in cytokine production or cytotoxicity between KIR+ and KIR- cells. My work has contributed to the literature on KIR biology in relation to lymphoid cells and will have direct relevance to a number of clinical studies

A Participatory Design Approach to Develop Visualization of Wearable Actigraphy Data for Health Care Professionals: Case Study in Qatar

BackgroundSeveral tools have been developed for health care professionals to monitor the physical activity of their patients, but most of these tools have been considering only the needs of users in North American and European countries and applicable for only specific analytic tasks. To our knowledge, no research study has utilized the participatory design (PD) approach in the Middle East region to develop such tools, involving all the stakeholders in the product development phases, and no clear use cases have been derived from such studies that could serve future development in the field. ObjectiveThis study aims to develop an interactive visualization tool (ActiVis) to support local health care professionals in monitoring the physical activity of their patients measured through wearable sensors, with the overall objective of improving the health of the Qatari population. MethodsWe used PD and user-centered design methodologies to develop ActiVis, including persona development, brainwriting, and heuristic walkthrough as part of user evaluation workshops; and use cases, heuristic walkthrough, interface walkthrough, and survey as part of expert evaluation sessions. ResultsWe derived and validated 6 data analysis use cases targeted at specific health care professionals from a collaborative design workshop and an expert user study. These use cases led to improving the design of the ActiVis tool to support the monitoring of patients’ physical activity by nurses and family doctors. The ActiVis research prototype (RP) compared favorably with the Fitbit Dashboard, showing the importance of design tools specific to end users’ needs rather than relying on repurposing existing tools designed for other types of users. The use cases we derived happen to be culturally agnostic, despite our assumption that the local Muslim and Arabic culture could impact the design of such visualization tools. At last, taking a step back, we reflect on running collaborative design sessions in a multicultural environment and oil-based economy. ConclusionsBeyond the development of the ActiVis tool, this study can serve other visualization and human–computer interaction designers in the region to prepare their design projects and encourage health care professionals to engage with designers and engineers to improve the tools they use for supporting their daily routine. The development of the ActiVis tool for nurses, and other visualization tools specific to family doctors and clinician researchers, is still ongoing and we plan to integrate them into an operational platform for health care professionals in Qatar in the near future

Investigating physiological glucose excursions before, during, and after Ramadan in adults without diabetes mellitus

© 2017 Elsevier Inc. Aim The study aimed to investigate physiological effects of Ramadan fasting on continuously monitored glucose levels in relation to Ramadan in young non-diabetic adults. Methods Continuous glucose monitoring was employed to measure interstitial glucose for several days 1–2 weeks before Ramadan, in the middle of Ramadan, and 4–6 weeks after Ramadan to assess glucose exposure and glucose variability. Results A total of 34,182 accurate glucose sensor readings and 438 capillary blood glucose values [mean absolute difference median (interquartile range) 8.5 (6.9–11.1)%] were obtained from 18 non-diabetic adults [13 females; aged 24 (21–27) years; baseline body mass index 23.9 (20.6–28.9) kg/m2]. The continuous glucose monitoring profiles showed an increase in the hyperglycemic (above 140 mg/dL) area under the curve after Ramadan compared to both before (P = 0.004) and during Ramadan (P = 0.003), along with an increased glucose variability after Ramadan (P = 0.014). Both the area under the interstitial glucose concentration curve for the entire day and the average glucose were positively associated with body mass index during (P = 0.004 and P = 0.005, respectively) and after Ramadan (P = 0.013 and P = 0.01, respectively). Atypical continuous glucose patterns were recognized in 11% of subjects, distinguished by a prolonged increased glucose exposure, particularly in response to a meal. Conclusion The time-point 4–6 weeks after Ramadan was distinguished by greater glucose exposure and wider glucose variability that may reflect ongoing changes in insulin sensitivity in response to altering lifestyle patterns in non-diabetic young adults across the spectrum of body weight

Perceptions and attitudes to clinical research participation in Qatar

Recruitment into clinical research studies is a major challenge. This study was carried out to explore the perceptions and attitudes towards clinical research participation among the general public in Qatar. A population based questionnaire study was carried out at public events held in Qatar. Residents of Qatar, 18 years or above in age were surveyed, anonymously, following verbal consent. Descriptive and multivariate analyses were conducted. We administered 2517 questionnaires to examine clinical research participation, of which 2379 complete forms were analyzed. Those who had previously been approached to participate in research completed a more detailed assessment. Data showed that only 5.7% participants (n = 134) had previously been approached to participate in a clinical research study. Of these 63.4% (n = 85) had agreed to participate while 36.6% (n = 49) had declined. The main reasons for declining participation included: time constraint (47.8%, n = 11), ‘fear’ (13.0%, n = 3), lack of awareness about clinical research (8.7%, n = 2) and lack of interest (8.7%, n = 2). ‘To help others’ (31.8%, n = 27) and ‘thought it might improve my access to health care’ (24.7%, n = 21) were the prime motivators for participation. There was a general agreement among participants that their previous research experience was associated with positive outcomes for self and others, that the research conduct was ethical, and that opportunities for participation will be welcomed in future. More than ten years of stay within Qatar was a statistically significant determinant of willingness to participate, adjusted odds ratio 5.82 (95% CI 1.93–17.55), p = 0.002. Clinical research participation in Qatar needs improvement. Time constraints, lack of trust in and poor awareness about clinical research are main barriers to participation. Altruism, and improved health access are reported as prime motivators. Deeper insight in to the factors affecting clinical research participation is needed to devise evidence based policies for improvement in recruitment strategies

Clinical and metabolic characteristics of the Diabetes Intervention Accentuating Diet and Enhancing Metabolism (DIADEM-I) randomised clinical trial cohort

Objectives Diabetes Intervention Accentuating Diet and Enhancing Metabolism-I (DIADEM-I) is the first randomised controlled trial (RCT) in the Middle East and North Africa (MENA) region testing the effectiveness of an intensive lifestyle intervention (ILI) for weight loss and diabetes remission. We report on the recruitment process and baseline characteristics of the DIADEM-I cohort based on origin (Middle East vs North Africa), and waist circumference.Design DIADEM-I is an open-label randomised, controlled, parallel group RCT recruiting young individuals (18–50 years) with early type 2 diabetes (≤3 years since diagnosis) originating from MENA. Individuals from primary care were randomised to usual medical care or ILI (total dietary replacement phase using meal replacement products, followed by staged food reintroduction and physical activity support). The primary outcome is weight loss at 12 months. Other outcomes are glycaemic control and diabetes remission.Setting Primary care, Qatar.Participants 147 (73% men) randomised within DIADEM-I who were included in the final trial data analysis.Outcome measures Recruitment metrics, and baseline clinical and metabolic characteristics.Results Of 1498 people prescreened, 267 (18%) were invited for screening and 209 (78%) consented. 173 (83%) were eligible. 15 (7%) withdrew before randomisation and the remaining 158 were randomised. Mean age was 42.1 (SD 5.6) years and mean body mass index was: 36.3 (5.5) kg/m2 (women) and 34.4 (5.4) kg/m2 (men). Mean diabetes duration was 1.8 (1.0) years and mean glycosylated haemoglobin (HbA1c) was 7.0% (1.30) (52.5 mmol/mol (SD 14.3)). Participants originated from 13 countries. Those from North Africa reported greater physical activity and had lower family history of diabetes. 90% of subjects were taking diabetes medications and 31% antihypertensives. Those with greater waist circumference had significantly higher insulin resistance and lower quality of life.Conclusion Recruitment of participants originating from the MENA region into the RCT was successful, and study participation was readily accepted. While DIADEM-I participants originated from 13 countries, there were few baseline differences amongst participants from Middle East versus North Africa, supporting generalisability of RCT results.Trial registration number ISRCTN20754766; NCT0322533